import numpy as np
import scipy.sparse as sparse
import scipy.sparse.linalg as linalg


class Solver(object):
    """
        Solver is a light wrapper on the various types of
        linear solvers available in python.

        :param scipy.sparse A: Matrix
        :param bool doDirect: if you want a direct solver
        :param string flag: Matrix type flag for special solves: [None, 'L', 'U', 'D']
        :param dict options: options which are passed to each sub solver, see each for details.
        :rtype: Solver
        :return: Solver

        To use for direct solvers::

            solve = Solver(A, doDirect=True, flag=None, options={'factorize':True,'backend':'scipy'})
            x = solve.solve(rhs)

        Or in one line::

            x = Solver(A).solve(rhs)

        The flag can be set to None, 'L', 'U', or 'D', for general, lower, upper, and diagonal matrices, respectively.

    """
    def __init__(self, A, doDirect=True, flag=None, options={}):
        assert type(doDirect) is bool, 'doDirect must be a boolean'
        assert flag in [None, 'L', 'U', 'D'], "flag must be set to None, 'L', 'U', or 'D'"

        self.A = A

        self.dsolve = None
        self.doDirect = doDirect
        self.flag = flag
        self.options = options

    def solve(self, b):
        """
            Solves the linear system.

            .. math::

                Ax=b

            :param numpy.ndarray b: the right hand side
            :rtype: numpy.ndarray
            :return: x
        """
        if self.flag is None and self.doDirect:
            return self.solveDirect(b, **self.options)
        elif self.flag is None and not self.doDirect:
            return self.solveIter(b, **self.options)
        elif self.flag == 'U':
            return self.solveBackward(b)
        elif self.flag == 'L':
            return self.solveForward(b)
        elif self.flag == 'D':
            return self.solveDiagonal(b)
        else:
            raise Exception('Unknown flag.')
        pass

    def clean(self):
        """Cleans up the memory"""
        del self.dsolve
        self.dsolve = None

    def solveDirect(self, b, factorize=False, backend='scipy'):
        """
            Use solve instead of this interface.

            :param bool factorize: if you want to factorize and store factors
            :param str backend: which backend to use. Default is scipy
            :rtype: numpy.ndarray
            :return: x
        """
        assert np.shape(self.A)[1] == np.shape(b)[0], 'Dimension mismatch'

        if factorize and self.dsolve is None:
            self.A = self.A.tocsc()  # for efficiency
            self.dsolve = linalg.factorized(self.A)

        if len(b.shape) == 1 or b.shape[1] == 1:
            # Just one RHS
            if factorize:
                return self.dsolve(b)
            else:
                return linalg.dsolve.spsolve(self.A, b)

        # Multiple RHSs
        X = np.empty_like(b)
        for i in range(b.shape[1]):
            if factorize:
                X[:,i] = self.dsolve(b[:,i])
            else:
                X[:,i] = linalg.dsolve.spsolve(self.A,b[:,i])

        return X

    def solveIter(self, b, M=None, iterSolver='CG'):
        pass

    def solveBackward(self, b, backend='python'):
        """
            Use solve instead of this interface.

            Perform a backwards solve with upper triangular A in CSR format (best, if not, it will be converted).

            :param str backend: which backend to use. Default is python.
            :rtype: numpy.ndarray
            :return: x
        """
        if type(self.A) is not sparse.csr.csr_matrix:
            from scipy.sparse import csr_matrix
            self.A = csr_matrix(self.A)
        vals = self.A.data
        rowptr = self.A.indptr
        colind = self.A.indices
        x = np.empty_like(b)   # empty() is faster than zeros().
        for i in reversed(xrange(self.A.shape[0])):
            ith_row = vals[rowptr[i] : rowptr[i+1]]
            cols = colind[rowptr[i] : rowptr[i+1]]
            x_vals = x[cols]
            x[i] = (b[i] - np.dot(ith_row[1:], x_vals[1:])) / ith_row[0]
        return x

    def solveForward(self, b, backend='python'):
        """
            Use solve instead of this interface.

            Perform a forward solve with lower triangular A in CSR format (best, if not, it will be converted).

            :param str backend: which backend to use. Default is python.
            :rtype: numpy.ndarray
            :return: x
        """
        if type(self.A) is not sparse.csr.csr_matrix:
            from scipy.sparse import csr_matrix
            self.A = csr_matrix(self.A)
        vals = self.A.data
        rowptr = self.A.indptr
        colind = self.A.indices
        x = np.empty_like(b)   # empty() is faster than zeros().
        for i in xrange(self.A.shape[0]):
            ith_row = vals[rowptr[i] : rowptr[i+1]]
            cols = colind[rowptr[i] : rowptr[i+1]]
            x_vals = x[cols]
            x[i] = (b[i] - np.dot(ith_row[:-1], x_vals[:-1])) / ith_row[-1]
        return x

    def solveDiagonal(self, b, backend='python'):
        """
            Use solve instead of this interface.

            Perform a diagonal solve with diagonal matrix A.

            :param str backend: which backend to use. Default is python.
            :rtype: numpy.ndarray
            :return: x
        """
        diagA = self.A.diagonal()
        if len(b.shape) == 1 or b.shape[1] == 1:
            # Just one RHS
            return b/diagA
        # Multiple RHSs
        X = np.empty_like(b)
        for i in range(b.shape[1]):
            X[:,i] = b[:,i]/diagA
        return X


if __name__ == '__main__':
    from SimPEG.mesh import TensorMesh
    from time import time
    h1 = np.ones(20)*100.
    h2 = np.ones(20)*100.
    h3 = np.ones(20)*100.

    h = [h1,h2,h3]

    M = TensorMesh(h)

    D = M.faceDiv
    G = M.cellGrad
    Msig = M.getFaceMass()
    A = D*Msig*G
    A[0,0] *= 10 # remove the constant null space from the matrix

    e = np.ones(M.nC)
    rhs = A.dot(e)

    tic = time()
    solve = Solver(A, options={'factorize':True})
    x = solve.solve(rhs)
    print 'Factorized', time() - tic
    print np.linalg.norm(e-x,np.inf)
    tic = time()
    solve = Solver(A, options={'factorize':False})
    x = solve.solve(rhs)
    print 'spsolve', time() - tic
    print np.linalg.norm(e-x,np.inf)